Comments (5)
This is a great idea, thanks! We should add this to the README. Our updated version for the symbolic regression task called uDSR, just published in NeurIPS 2022 (code update to follow), tops SRBench by a large margin: https://openreview.net/forum?id=2FNnBhwJsHK
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We updated our repo and now include those results in our README :)
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From an initial glance, *DSR seems to do pretty well https://arxiv.org/abs/2107.14351
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How close is this release to what was used in the benchmark and NeurIPS2022? For example, I see the linear token but does the code use AI Feynman to create subproblems?
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@tluchko Good point, unfortunately we weren't able to include the AIF or LSPT components in this release. Though if you look at the ablations in our paper, it hardly makes a difference once you have enough components. Probably just better off running DSR + GP + LINEAR (which are all included in this release) for longer.
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Related Issues (20)
- create primitive set mapping name clash HOT 2
- Deap varying with constraints HOT 4
- Methods for Running DSO HOT 4
- Normalization for input variables to domain (0,1) HOT 1
- Normalization for input variables to domain (0,1) HOT 1
- Different Learned Equation with Different Numpy Array Shape HOT 4
- Doubts about output HOT 1
- Ratio of training set to testing set HOT 1
- training iteration N, current best R : 0.9 HOT 1
- Trouble Installing, sample virtual environment config? HOT 7
- How to install configuration and generate an interactive platform? HOT 1
- Defining custom gaussian function HOT 3
- Ignoring errors... HOT 1
- adjusting best R or custom best R HOT 1
- how to run dso multiple times in a loop HOT 2
- How can parallel computing improve speed in a single-task scenario? HOT 1
- Installation error HOT 3
- about putting constraints HOT 2
- [Feature Request] Is it possiable to specify assertion on the expression to generate?
- The gp meld method in sklearn interface
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